feat(core): 完成 Phase 2 核心功能开发

- 实现查询API (query.py): 支持star_id/unique_id/nickname三种查询方式 - 实现计算模块 (calculator.py): CPM/自然搜索UV/搜索成本计算 - 实现品牌API集成 (brand_api.py): 批量并发调用，10并发限制 - 实现导出服务 (export_service.py): Excel/CSV导出 - 前端组件: QueryForm/ResultTable/ExportButton - 主页面集成: 支持6种页面状态 - 测试: 44个测试全部通过，覆盖率88% Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-28 14:38:38 +08:00
parent ac0f086821
commit 8fbcb72a3f
21 changed files with 1677 additions and 100 deletions
@@ -0,0 +1,59 @@
+from datetime import datetime
+from typing import Literal
+
+from fastapi import APIRouter, Query
+from fastapi.responses import StreamingResponse
+from io import BytesIO
+
+from app.services.export_service import generate_excel, generate_csv
+
+router = APIRouter()
+
+# 存储最近的查询结果 (简化实现, 生产环境应使用 Redis 等缓存)
+_cached_data: list = []
+
+
+def set_export_data(data: list):
+    """设置导出数据缓存."""
+    global _cached_data
+    _cached_data = data
+
+
+def get_export_data() -> list:
+    """获取导出数据缓存."""
+    return _cached_data
+
+
+@router.get("/export")
+async def export_data(
+    format: Literal["xlsx", "csv"] = Query("xlsx", description="导出格式"),
+):
+    """
+    导出查询结果.
+
+    Args:
+        format: 导出格式 (xlsx 或 csv)
+
+    Returns:
+        文件下载响应
+    """
+    data = get_export_data()
+
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+
+    if format == "xlsx":
+        content = generate_excel(data)
+        media_type = "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+        filename = f"kol_data_{timestamp}.xlsx"
+    else:
+        content = generate_csv(data)
+        media_type = "text/csv; charset=utf-8"
+        filename = f"kol_data_{timestamp}.csv"
+
+    return StreamingResponse(
+        BytesIO(content),
+        media_type=media_type,
+        headers={
+            "Content-Disposition": f'attachment; filename="{filename}"',
+        },
+    )
@@ -0,0 +1,66 @@
+from fastapi import APIRouter, Depends
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.database import get_db
+from app.schemas.query import QueryRequest, QueryResponse, VideoData
+from app.services.query_service import query_videos
+from app.services.calculator import calculate_metrics
+from app.services.brand_api import get_brand_names
+from app.api.v1.export import set_export_data
+
+router = APIRouter()
+
+
+@router.post("/query", response_model=QueryResponse)
+async def query(
+    request: QueryRequest,
+    db: AsyncSession = Depends(get_db),
+) -> QueryResponse:
+    """
+    批量查询 KOL 视频数据.
+
+    支持三种查询方式:
+    - star_id: 按星图ID精准匹配
+    - unique_id: 按达人unique_id精准匹配
+    - nickname: 按达人昵称模糊匹配
+    """
+    try:
+        # 1. 查询数据库
+        videos = await query_videos(db, request.type, request.values)
+
+        if not videos:
+            return QueryResponse(success=True, data=[], total=0)
+
+        # 2. 提取品牌ID并批量获取品牌名称
+        brand_ids = [v.brand_id for v in videos if v.brand_id]
+        brand_map = await get_brand_names(brand_ids) if brand_ids else {}
+
+        # 3. 转换为响应模型并计算指标
+        data = []
+        for video in videos:
+            video_data = VideoData.model_validate(video)
+
+            # 填充品牌名称
+            if video.brand_id:
+                video_data.brand_name = brand_map.get(video.brand_id, video.brand_id)
+
+            # 计算预估指标
+            metrics = calculate_metrics(
+                estimated_video_cost=video.estimated_video_cost,
+                natural_play_cnt=video.natural_play_cnt,
+                total_play_cnt=video.total_play_cnt,
+                after_view_search_uv=video.after_view_search_uv,
+            )
+            video_data.estimated_natural_cpm = metrics["estimated_natural_cpm"]
+            video_data.estimated_natural_search_uv = metrics["estimated_natural_search_uv"]
+            video_data.estimated_natural_search_cost = metrics["estimated_natural_search_cost"]
+
+            data.append(video_data)
+
+        # 缓存数据供导出使用
+        set_export_data([d.model_dump() for d in data])
+
+        return QueryResponse(success=True, data=data, total=len(data))
+
+    except Exception as e:
+        return QueryResponse(success=False, data=[], total=0, error=str(e))